Von Willebrand Factor (VWF) multiplex activity assay differentiation of type 1 von Willebrand Disease (VWD) and variant VWD
Bibliographic record
Abstract
INTRODUCTION: VWD diagnosis is challenging requiring multiple VWF activity tests using many individual assays. We have developed an ELISA-based VWF Multiplex Activity Assay (VWF-MAA) to address this concern; however, the ability of the VWF-MAA to discriminate between type 1 VWD, variant VWD, and normal subjects has not been evaluated. AIM: To evaluate the VWF-MAA and its ability to differentiate between type 1 VWD, variant VWD and normal subjects in individuals undergoing an initial laboratory evaluation for bleeding. METHODS: A total of 177 plasma samples from the Zimmerman Program: Comparative Effectiveness in the Diagnosis of VWD were evaluated from 11 centres across the US and Canada. The VWF-MAA was compared to Versiti Blood Research Institute (VBRI) and Local Center (LC) assigned VWD diagnosis. RESULTS: Overall, 129/177 (72.9%) were correctly assigned as normal (non-VWD), type 1, or variant VWD compared to the VBRI assigned diagnosis. VWF-MAA assigned non-VWD accurately in 29/57 (50.9%) samples, and type 1 VWD accurately in 93/110 (84.6%) samples. Considering LC diagnosis where there was agreement with VWF-MAA and not VBRI diagnosis, type 1 VWD was accurate in 105/110 (95.5%) samples. Bland-Altman analysis demonstrated good correlation between laboratory methods. VWD, types 2A, 2B, 1C VWD were also assigned by the VWF-MAA. CONCLUSIONS: We demonstrate that the VWF-MAA has utility in differentiating type 1 VWD, variant VWD and normal subjects in individuals undergoing an initial laboratory evaluation for bleeding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".